Plotting Scenario Data¶

The plotting functions use the analysis modules to process data. We have a demo folder that encloses numerous notebooks that we hope will help you analyze/display your scenario data.

Single Scenario¶

Transmission¶

get a power flow snapshot (notebook)

from bokeh.io import show
import pandas as pd

from postreise.plot.plot_powerflow_snapshot import plot_powerflow_snapshot
from powersimdata import Scenario

scenario = Scenario(3287)
grid = scenario.get_grid()

pf_map = plot_powerflow_snapshot(
  scenario, pd.Timestamp(2016, 11, 2, 22), legend_font_size=20
)
show(pf_map)

Emission¶

show carbon emission by generator type using circles overlay on a map (notebook)
```
from bokeh.io import show

from postreise.plot.plot_carbon_map import map_carbon_emission_generator
from powersimdata import Scenario

scenario = Scenario(3287)

emission_map = map_carbon_emission_generator(
  scenario, coordinate_rounding=0, scale_factor=0.75
)
show(emission_map)
```
Bokeh Plot

Generation¶

plot stacked generation time series in an area (notebook)

from postreise.plot.plot_generation_ts_stack import (
  plot_generation_time_series_stack,
)
from powersimdata import Scenario

t2c = {
  "nuclear": "#173FA5",
  "hydro": "#0090FF",
  "geothermal": "#CC67F3",
  "other": "#8B36FF",
  "dfo": "#31E8CB",
  "coal": "#37404C",
  "ng": "#72818F",
  "solar": "#FFBB45",
  "wind": "#78D911",
  "solar_curtailment": "#FFBB45",
  "wind_curtailment": "#78D911",
}

t2l = {
    "nuclear": "Nuclear",
    "hydro": "Hydro",
    "geothermal": "Geothermal",
    "other": "Other",
    "dfo": "Distillate Fuel Oil",
    "coal": "Coal",
    "ng": "Natural Gas",
    "solar": "Solar",
    "wind": "Wind",
    "wind_offshore": "Wind Offshore",
    "biomass": "Biomass",
    "storage": "Storage",
    "solar_curtailment": "Solar Curtailment",
    "wind_curtailment": "Wind Curtailment",
    "wind_offshore_curtailment": "Offshore Wind Curtailment",
}

t2hc = {
    "solar_curtailment": "#996100",
    "wind_curtailment": "#4e8e0b"
}

scenario = Scenario(1171)

resources = [
  "nuclear",
  "coal",
  "hydro",
  "geothermal",
  "other",
  "dfo",
  "ng",
  "solar",
  "wind",
  "storage",
  "solar_curtailment",
  "wind_curtailment",
]

plot_generation_time_series_stack(
  scenario, "Western", resources, time_freq="D", normalize=True, t2c=t2c, t2l=t2l,
  t2hc=t2hc
)

../_images/generation_stack_western_ts.png

Curtailment¶

plot renewable generators curtailment time series in an area (notebook)

import matplotlib.pyplot as plt

from postreise.plot.plot_curtailment_ts import plot_curtailment_time_series
from powersimdata import Scenario

scenario = Scenario(403)

t2c = {"wind_curtailment": "blue", "solar_curtailment": "blue"}

plot_curtailment_time_series(
  scenario, "Eastern", ["wind","solar"], time_freq='D', t2c=t2c,
  label_fontsize=30, title_fontsize=35, tick_fontsize=25, legend_fontsize=25,
)
plt.show()

../_images/curtailment_solar_eastern_ts.png

../_images/curtailment_wind_eastern_ts.png

Price¶

map locational marginal price (notebook)
```
from bokeh.io import show

from postreise.plot.plot_lmp_map import map_lmp
from powersimdata import Scenario

scenario = Scenario(3287)

lmp_map = map_lmp(scenario)
show(lmp_map)
```
Bokeh Plot

General¶

plot any time-series values using a heatmap where each column is one color-coded day (notebook)

import matplotlib.pyplot as plt
from powersimdata import Scenario

from postreise.analyze.generation.curtailment import (
  calculate_curtailment_time_series,
)
from postreise.plot.plot_heatmap import plot_heatmap

scenario = Scenario(3287)
curtailment = calculate_curtailment_time_series(scenario).sum(axis=1)

plot_heatmap(
    curtailment,
    cmap="PiYG_r",
    scale=1e-3,
    cbar_label="GW",
    vmin=0,
    vmax=250,
    cbar_tick_values=[0, 50, 100, 150, 200, 250],
    cbar_tick_labels=['0', '50', '100', '150', '200', '≥250'],
    time_zone="ETC/GMT+6",
    time_zone_label="(CST)",
    contour_levels=[250],
)

map transmission lines color coded by interconnection
```
from bokeh.io import show

from postreise.plot.plot_interconnection_map import map_interconnections
from powersimdata import Scenario

scenario = Scenario(3287)
grid = scenario.get_grid()

transmission_map = map_interconnections(grid)
show(transmission_map)
```
Bokeh Plot

Scenarios Comparison¶

Generator Capacity and Generation¶

compare generation and capacity in various scenarios through bar charts (notebook)

from postreise.plot.plot_bar_generation_vs_capacity import (
  plot_bar_generation_vs_capacity,
)
from powersimdata.utility.helpers import PrintManager

with PrintManager():
    plot_bar_generation_vs_capacity(
        areas=["CA", "Western"],
        scenario_ids=[2497, 3101],
        scenario_names=[
            "Western 90% clean and 10% nuclear",
            "Western 90% clean and 10% nuclear with transmission upgrade",
        ],
    )

../_images/capacity_vs_generation_ca_bar.png

../_images/capacity_vs_generation_western_bar.png

compare generation and capacity in various scenarios through pie charts (notebook)

from postreise.plot.plot_pie_generation_vs_capacity import (
  plot_pie_generation_vs_capacity,
)
from powersimdata.utility.helpers import PrintManager

with PrintManager():
    plot_pie_generation_vs_capacity(
        areas=["WA", "Western"],
        scenario_ids=[2497, 3101],
        scenario_names=[
            "Western 90% clean and 10% nuclear",
            "Western 90% clean and 10% nuclear \n with transmission upgrade",
        ],
    )

../_images/capacity_vs_generation_wa_pie.png

../_images/capacity_vs_generation_western_pie.png

compare generation shortfall in various scenarios through bar charts (notebook)

import inspect
import os
from powersimdata.utility.helpers import PrintManager

import postreise
from postreise.plot.plot_bar_shortfall import plot_bar_shortfall
from powersimdata.design.generation.clean_capacity_scaling import (
  load_targets_from_csv,
)

data = os.path.join(os.path.dirname(inspect.getfile(postreise)), "data")
target = load_targets_from_csv(
    os.path.join(data, "2030_USA_Clean_Energy_Regular_Targets.csv")
)
with PrintManager():
    plot_bar_shortfall(
        "Nevada",
        [2497, 3101],
        target,
        scenario_names=[
            "Western 90% clean and 10% nuclear",
            "Western 90% clean and 10% nuclear"
            + "\n"
            + "with transmission upgrade",
        ],
        baseline_scenario=2497,
        baseline_scenario_name="Western 90% clean and 10% nuclear",
    )

Emission¶

compare total carbon emissions by generator type for 1-to-n scenarios through bar charts (notebook)

from postreise.plot.plot_carbon_bar import plot_carbon_bar
from powersimdata import Scenario
from powersimdata.utility.helpers import PrintManager

scenarioA = Scenario(2497)
scenarioB = Scenario(3101)

with PrintManager():
    scenarioA = Scenario(2497)
    scenarioB = Scenario(3101)

plot_carbon_bar(
    scenarioA, scenarioB,
    labels=["Western" + "\n" + "90% clean and 10% nuclear",
            "Western" + "\n" + "90% clean and 10% nuclear"
            + "\n"
            + "with transmission upgrade"]
)

compare carbon emission by generator type for two scenarios on a map (notebook)
```
from bokeh.io import show

from postreise.plot.plot_carbon_map import map_carbon_emission_difference
from powersimdata import Scenario

scenarioA = Scenario(2497)
scenarioB = Scenario(3101)

emission_difference_map = map_carbon_emission_difference(
  scenarioA, scenarioB, coordinate_rounding=0
)
show(emission_difference_map)
```
Bokeh Plot

plot stacked generation and carbon emission for 1-to-n scenarios side-by-side (notebook)

from postreise.plot.plot_energy_carbon_stack import plot_n_scenarios
from powersimdata import Scenario

scenarioA = Scenario(2497)
scenarioB = Scenario(3101)

plot_n_scenarios(scenarioA, scenarioB)

../_images/energy_emission_stack_bar.png

Plotting Scenario Data¶

Single Scenario¶

Transmission¶

Emission¶

Generation¶

Curtailment¶

Price¶

General¶

Scenarios Comparison¶

Generator Capacity and Generation¶

Emission¶

Navigation

Related Topics

Useful Links

Code